Access the full text.
Sign up today, get DeepDyve free for 14 days.
A. Phinyomark, R. Khushaba, E. Scheme (2018)
Feature Extraction and Selection for Myoelectric Control Based on Wearable EMG SensorsSensors (Basel, Switzerland), 18
Shili Liang, Yansheng Wu, Jianfei Chen, Ling Zhang, Peipei Chen, Zongqian Chai, Chunlei Cao (2019)
Identification of Gesture Based on Combination of Raw sEMG and sEMG Envelope Using Supervised Learning and Univariate Feature SelectionJournal of Bionic Engineering, 16
Chih-Chung Chang, Chih-Jen Lin (2011)
LIBSVM: A library for support vector machinesACM Trans. Intell. Syst. Technol., 2
aria Hakonena, Harri Piitulainenb, Arto Visalaa (2015)
urrent state of digital signal processing in myoelectric interfaces and elated applications
P. Kaczmarek, Tomasz Mankowski, J. Tomczynski (2019)
putEMG—A Surface Electromyography Hand Gesture Recognition DatasetSensors (Basel, Switzerland), 19
(2006)
PalaniswamiM. Hand Gestures for HCI Using ICA of EMG. Proceedings of the HCSNet workshop on use of vision in human-computer interaction - volume 56, Darlinghurst, Australia, Australia
T. Saponas, Desney Tan, Dan Morris, Jim Turner, J. Landay (2010)
Making muscle-computer interfaces more practicalProceedings of the SIGCHI Conference on Human Factors in Computing Systems
G. Naik (2011)
A comparison of ICA algorithms in surface EMG signal processingInternational Journal of Biomedical Engineering and Technology, 6
G. Naik, D. Kumar (2011)
An Overview of Independent Component Analysis and Its ApplicationsInformatica (Slovenia), 35
N. Nazmi, Mohd Rahman, Shin-ichiroh Yamamoto, S. Ahmad, H. Zamzuri, S. Mazlan (2016)
A Review of Classification Techniques of EMG Signals during Isotonic and Isometric ContractionsSensors (Basel, Switzerland), 16
G. Naik, D. Kumar, M. Palaniswami (2008)
Multi run ICA and surface EMG based signal processing system for recognising hand gestures2008 8th IEEE International Conference on Computer and Information Technology
J. Mendes, R. Robson, S. Labidi, A. Barros (2008)
Subvocal Speech Recognition Based on EMG Signal Using Independent Component Analysis and Neural Network MLP2008 Congress on Image and Signal Processing, 1
José Junior, M. Freitas, H. Siqueira, A. Lazzaretti, S. Pichorim, S. Stevan (2020)
Feature selection and dimensionality reduction: An extensive comparison in hand gesture classification by sEMG in eight channels armband approachBiomed. Signal Process. Control., 59
Aapo Hyvärinen, E. Oja (2000)
Independent component analysis: algorithms and applicationsNeural networks : the official journal of the International Neural Network Society, 13 4-5
Róisín Howard, R. Conway, A. Harrison (2015)
The use of Independent component analysis on EMG data to explore cross-talk
M. Freitas, José MendesJr., D. Campos, Sergio StevanJr. (2019)
Hand Gestures Classification Using Multichannel sEMG ArmbandXXVI Brazilian Congress on Biomedical Engineering
Enrico Costanza, Samuel Inverso, Rebecca Allen, P. Maes (2007)
Intimate interfaces in action: assessing the usability and subtlety of emg-based motionless gesturesProceedings of the SIGCHI Conference on Human Factors in Computing Systems
G. Naik, D. Kumar, Vijay Singh, M. Palaniswami (2018)
SEMG for Identifying Hand Gestures using ICA
Shang Xiaojing, T. Yantao, Li Yang (2011)
Feature extraction and classification of sEMG based on ICA and EMD decomposition of AR model2011 International Conference on Electronics, Communications and Control (ICECC)
J. Mak, Yong Hu, K. Luk (2010)
An automated ECG-artifact removal method for trunk muscle surface EMG recordings.Medical engineering & physics, 32 8
D. Toledo-Pérez, J. Rodríguez-Reséndiz, R. Gómez-Loenzo, J. Jáuregui-Correa (2019)
Support Vector Machine-Based EMG Signal Classification Techniques: A ReviewApplied Sciences
G. Naik, D. Kumar, Vijay Singh, M. Palaniswami (2006)
Hand gestures for HCI using ICA of EMG
D. Sueaseenak, Theerasak Chanwimalueang, M. Sangworasil, C. Pintavirooj (2009)
An investigation of robustness in independent component analysis EMG2009 6th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology, 02
G. Naik, D.K. Kumar, H. Weghorn (2007)
Performance comparison of ICA algorithms for Isometric Hand gesture identification using Surface EMG2007 3rd International Conference on Intelligent Sensors, Sensor Networks and Information
Hideo Nakamura, Masaki Yoshida, M. Kotani, K. Akazawa, T. Moritani (2004)
The application of independent component analysis to the multi-channel surface electromyographic signals for separation of motor unit action potential trains: part I-measuring techniques.Journal of electromyography and kinesiology : official journal of the International Society of Electrophysiological Kinesiology, 14 4
G. Naik, D. Kumar, S. Arjunan, H. Weghorn, M. Palaniswami (2007)
Limitations and Applications of ICA in Facial sEMG and Hand Gesture sEMG for Human Computer Interaction9th Biennial Conference of the Australian Pattern Recognition Society on Digital Image Computing Techniques and Applications (DICTA 2007)
G. Naik, Ali Al-timemy, Hung Nguyen (2016)
Transradial Amputee Gesture Classification Using an Optimal Number of sEMG Sensors: An Approach Using ICA ClusteringIEEE Transactions on Neural Systems and Rehabilitation Engineering, 24
Andrés Jaramillo-Yánez, Marco Benalcázar, Elisa Mena-Maldonado (2020)
Real-Time Hand Gesture Recognition Using Surface Electromyography and Machine Learning: A Systematic Literature ReviewSensors (Basel, Switzerland), 20
Publisher's note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations
Multi-channel surface electromyography acquisition is widely applied for gesture recognition in wearable armband devices and it may contain redundant information between the channels. An issue in this technique is the number of channels, which can improve the classification accuracy, but it requires more computational effort. Independent component analysis is a signal processing technique used in blind source separation problems and can be used to reduce dimension, separating linearly mixed sources. In this study, an analysis of the influence of independent component analysis in hand gesture classification with surface electromyography signals, acquired from the forearm, is proposed. Six gestures were acquired from 10 subjects, 4 time-domain features were extracted, and five classifiers were used in the evaluation. This work compares two approaches to extract the W matrix (the demixing matrix) in independent component analysis: one W matrix for each subject and one W for each gesture sample. Besides, the effect of increasing the number of channels in the classification is analyzed, aiming to find a statistically relevant number of independent components. The results showed that the two approaches of W matrix have no significant difference between them. Moreover, it was observed that the number of independent components affects the classifiers, but five components showed the same distribution of results compared with more components. Concerning the classifiers, extreme learning machine neural network and support vector machines presented the best results (over 90%).
Research on Biomedical Engineering – Springer Journals
Published: Aug 15, 2020
Read and print from thousands of top scholarly journals.
Already have an account? Log in
Bookmark this article. You can see your Bookmarks on your DeepDyve Library.
To save an article, log in first, or sign up for a DeepDyve account if you don’t already have one.
Copy and paste the desired citation format or use the link below to download a file formatted for EndNote
Access the full text.
Sign up today, get DeepDyve free for 14 days.
All DeepDyve websites use cookies to improve your online experience. They were placed on your computer when you launched this website. You can change your cookie settings through your browser.